Multi-Agent Artificial Intelligence Model to Enhance Self-Regulated Learning and Conceptual Understanding in Computer Science Education

Abstract

This study explores the integration of a multi-agent AI system designed to support self-regulated learning (SRL) in computer science education. Building on preliminary work that introduced a Teaching Assistant AI (TA-AI) for real-time scaffolding, an Analytics-AI for pattern recognition, and an AI-Improver for iterative refinement, this study evaluates the system’s empirical impact on student help-seeking behavior. Guided by fundamental SRL principles, this study analyzed data from 38 graduate students across 109 help-seeking attempts to investigate the factors influencing prompting strategies during system implementation. Findings showed that students utilized the TA-AI as a strategic safety net. Questions where students sought help were associated with significantly lower correctness (OR = 0.35, p < .001). A “sweet spot” in quiz difficulty was identified where the TA-AI is most effective in activating metacognitive prompting: medium-difficulty questions significantly increased the likelihood of